Body orientation/motion monitoring and particularly fall monitoring is very useful to a wide range of subjects, from elderly persons to emergency workers to race horses to anthropomorphic robots. While the ability to detect a hard impact fall is important in all cases, additional information about “soft” falls, such as, for example, when a person is overcome by smoke and slowly sinks to the ground, or near-falls, as in when a race horse stumbles, are also important events to detect. Prior fall detection and monitoring technology has taken many forms. Some previous inventions have employed accelerometers in a waist-worn device to detect human falls. Although wrist-worn devices with accelerometers are relatively successful at detecting some “hard” falls, which are defined as sudden and rapid descents that are usually associated with a high-g impact, these devices have numerous drawbacks, including, for example, difficulty detecting backward and lateral falls and an inability to sense the difference between a person lying down and a person who has fallen. Thus, these prior systems cannot be used when a person is at rest or when a person experiences a “soft” fall without an impact (e.g., a firefighter that is slowly overcome by smoke and slowly sinks to the ground). Furthermore, these systems may completely miss near-fall events, such as, for example, when an elderly person trips and grabs onto a railing in order to avoid a fall or a stumble or gait change event that might precede a hard fall. In fact, prior work, such as, e.g., the commercially available system described at <<http://www.dynamic-living.com/pers-info.htm#fall>> can include disclaimers like “The Fall Detector cannot differentiate between a fall and simply lying down to rest. You will need to remove it before you lie down and then put it back on when you get up.”
An example of a device presently available commercially that can monitor body orientation and movement patterns in free-living subjects is the Intelligent Device for Energy Expenditure and Physical Activity (IDEEA) (Minisun, Fresno, Calif.), as described in Zhang K, Werner P, Sun M, Pi-Sunyer F X, Boozer C N, “Measurement of human daily physical activity,” Obes Res, Jan. 11, 2003(1):33-40. This accelerometer-based device is costly and requires the use of five sensors that are taped directly to the skin (e.g., chest, both thighs, and bottom of both feet) and associated cable tethers to attach the sensors to a waist-worn data-recording unit. The manufacturer claims that the IDEEA can identify various body postures and types of physical activity, including lying, sitting, walking, climbing stairs, running, and jumping.
Another example is the BodyTrac system (IMSystems, Baltimore, Md.), which includes a body posture and movement pattern recorder nominally worn as a chest band. The BodyTrack system employs a 5.8 cm×3.4 cm×1.7 cm module containing a sealed sphere with a bolus of mercury that, depending on body posture, short different sets of contacts. The BodyTrac system is claimed to provide the following body posture information: upright, walking, lying supine, lying right, lying left, and lying prone. A major limitation of the BodyTrac system appears to be an inability to discriminate between sitting and standing; to accomplish this, the BodyTrac system employs a second monitor that must be worn on a thigh, as described by Gorny S W, Allen R P, Krausman D T, Cammarata J., “Initial demonstration of the accuracy and utility of an ambulatory, three-dimensional body position monitor with normals, sleepwalkers and restless legs patients,” Sleep Med, March 2001; 2(2):135-143.
Other currently available devices used to monitor falls, such as, e.g., described in Pervasive Computing, “A Smart Sensor to Detect the Falls of the Elderly,” Sixsmith, Andrew; Johnson, Neil Vol. 3, No 2 pp 42-47, include sophisticated visual monitoring systems and “smart homes” that feature sensors embedded in floors or infrared beams positioned near the floor. Although some of these systems may provide autonomous notification aspects, none allow for the detection of near falls, and none would allow for the detection of falls outside of the subject's home. Therefore, these devices would not prove useful for emergency workers, individuals with seizures, race horses, or the like.
Thus, a long felt, unfulfilled need exists for an apparatus, system and method that may monitor and detect a fall event type by a user, such as, for example, but not limited to, an emergency worker, an individual with a medical condition, a race horse, or the like.